How machine learning can drive retail success

After retailers suffered a bad year with bankruptcies, store closures and lower store footfall, we discuss why now is the time for retailers to invest in data and advanced technologies to boost consumer relations.

Bricks and mortar retailers would sooner forget 2018. The year that brought 16 U.S. bankruptcies, falling share prices for leading European brands, and the UK’s worst festive sales in 10 years isn’t an industry high point. But it does offer a crucial lesson for those struggling in a tough climate: the need to harness digital.

While footfall in stores continues to decline, online retail continues to thrive globally, with annual sales exceeding $2.4 trillion. Clearly, web-based buying is no fad; it’s a growing market shift retailers must embrace if they want to survive. And a key requirement to ensure effectiveness will be tailored advertising that reaches the right individuals, at the ideal moment.

The question is: how can retailers obtain the insights they need to create relevant ad campaigns that drive digital success?

Foreseeing the unpredictable

One answer lies in leveraging precious consumer data with smart technology to make sense of shopper habits. As most retailers know, customer journeys now cover a myriad of real-world and digital channels, such as websites, apps and physical stores, as well as needs and interests. So, it can be hard to track individuals, let alone establish which ads have the best chance of achieving in-the-moment impact.

Yet the good news is that advances in machine learning (ML) have made it possible to address these issues via large-scale data collection and analysis, which provides real-time insight into shopper behaviour. In particular, more autonomous branches of ML — such as Reinforcement Learning (RL) — are using data about previous consumer activity to understand individuals and predict their likely responses to specific ads.

RL: the quick-fire essentials

In short, RL finds the best possible action in certain contexts. The basic mechanics involve training RL algorithms to master a specific type of problem solving: artificial agents must determine the ideal decision in their current state by assessing options open to them, and the positive or negative rewards they bring. Think of it as similar to a popular maze-based arcade game; the user’s goal is collating points, but during navigation they meet ghosts (bad rewards) and power boosters (good rewards) that need to be evaluated, except the RL jackpot is a key performance indicator (KPI).

How can it improve retailer fortunes?

The key benefit of RL is also its defining characteristic: adaptability. Unlike standard ML, where agents behave according to set rules for each possible scenario, RL rewards aren’t instant and the environment shifts after every action. As a result, maze boundaries aren’t fixed and agents learn how to act for themselves: using rewards to create optimal strategies for reaching a final objective. And this makes RL a perfect foundation for real-time ad targeting in today’s dynamic retail landscape.

When the huge processing ability of RL is applied to consumer insight — including first party data about website visits, ad exposure, past purchases, brand interactions and location — retailers can immediately pinpoint behavioural patterns that inform meaningful and effective advertising. For example, analysis might show an individual has regularly shared social video ads and bought featured products, indicating this format is the most likely to be positively received and drive a sale.

Plus, RL algorithms can be trained to meet particular KPIs when targeting specific consumers, with accuracy constantly improving as positive rewards refine strategy, ad serving frequency, and format selection. Over time the information gathered can be used to predict wider trends among your target audience helping inform advertising budgets and campaign direction.

Ultimately, RL has the potential to create a more engaging and balanced advertising experience that is better for everyone. With a greater understanding of what works for individuals, and what doesn’t, retailers can fine-tune their approach to deliver ads in the right quantity and medium, and to the most appropriate channels or devices – instead of bombarding target audiences with irrelevant and disruptive messages. And in doing so they will not only forge deeper personal bonds that drive positive brand perception and loyalty, but also ensure continued success by proving they can deliver the personalisation consumers want, only when they want it.